6,290 research outputs found

    Machine learning in solar physics

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    The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

    Segmentation of Pathology Images: A Deep Learning Strategy with Annotated Data

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    Cancer has significantly threatened human life and health for many years. In the clinic, histopathology image segmentation is the golden stand for evaluating the prediction of patient prognosis and treatment outcome. Generally, manually labelling tumour regions in hundreds of high-resolution histopathological images is time-consuming and expensive for pathologists. Recently, the advancements in hardware and computer vision have allowed deep-learning-based methods to become mainstream to segment tumours automatically, significantly reducing the workload of pathologists. However, most current methods rely on large-scale labelled histopathological images. Therefore, this research studies label-effective tumour segmentation methods using deep-learning paradigms to relieve the annotation limitations. Chapter 3 proposes an ensemble framework for fully-supervised tumour segmentation. Usually, the performance of an individual-trained network is limited by significant morphological variances in histopathological images. We propose a fully-supervised learning ensemble fusion model that uses both shallow and deep U-Nets, trained with images of different resolutions and subsets of images, for robust predictions of tumour regions. Noise elimination is achieved with Convolutional Conditional Random Fields. Two open datasets are used to evaluate the proposed method: the ACDC@LungHP challenge at ISBI2019 and the DigestPath challenge at MICCAI2019. With a dice coefficient of 79.7 %, the proposed method takes third place in ACDC@LungHP. In DigestPath 2019, the proposed method achieves a dice coefficient 77.3 %. Well-annotated images are an indispensable part of training fully-supervised segmentation strategies. However, large-scale histopathology images are hardly annotated finely in clinical practice. It is common for labels to be of poor quality or for only a few images to be manually marked by experts. Consequently, fully-supervised methods cannot perform well in these cases. Chapter 4 proposes a self-supervised contrast learning for tumour segmentation. A self-supervised cancer segmentation framework is proposed to reduce label dependency. An innovative contrastive learning scheme is developed to represent tumour features based on unlabelled images. Unlike a normal U-Net, the backbone is a patch-based segmentation network. Additionally, data augmentation and contrastive losses are applied to improve the discriminability of tumour features. A convolutional Conditional Random Field is used to smooth and eliminate noise. Three labelled, and fourteen unlabelled images are collected from a private skin cancer dataset called BSS. Experimental results show that the proposed method achieves better tumour segmentation performance than other popular self-supervised methods. However, by evaluated on the same public dataset as chapter 3, the proposed self-supervised method is hard to handle fine-grained segmentation around tumour boundaries compared to the supervised method we proposed. Chapter 5 proposes a sketch-based weakly-supervised tumour segmentation method. To segment tumour regions precisely with coarse annotations, a sketch-supervised method is proposed, containing a dual CNN-Transformer network and a global normalised class activation map. CNN-Transformer networks simultaneously model global and local tumour features. With the global normalised class activation map, a gradient-based tumour representation can be obtained from the dual network predictions. We invited experts to mark fine and coarse annotations in the private BSS and the public PAIP2019 datasets to facilitate reproducible performance comparisons. Using the BSS dataset, the proposed method achieves 76.686 % IOU and 86.6 % Dice scores, outperforming state-of-the-art methods. Additionally, the proposed method achieves a Dice gain of 8.372 % compared with U-Net on the PAIP2019 dataset. The thesis presents three approaches to segmenting cancers from histology images: fully-supervised, unsupervised, and weakly supervised methods. This research effectively segments tumour regions based on histopathological annotations and well-designed modules. Our studies comprehensively demonstrate label-effective automatic histopathological image segmentation. Experimental results prove that our works achieve state-of-the-art segmentation performances on private and public datasets. In the future, we plan to integrate more tumour feature representation technologies with other medical modalities and apply them to clinical research

    Novel 129Xe Magnetic Resonance Imaging and Spectroscopy Measurements of Pulmonary Gas-Exchange

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    Gas-exchange is the primary function of the lungs and involves removing carbon dioxide from the body and exchanging it within the alveoli for inhaled oxygen. Several different pulmonary, cardiac and cardiovascular abnormalities have negative effects on pulmonary gas-exchange. Unfortunately, clinical tests do not always pinpoint the problem; sensitive and specific measurements are needed to probe the individual components participating in gas-exchange for a better understanding of pathophysiology, disease progression and response to therapy. In vivo Xenon-129 gas-exchange magnetic resonance imaging (129Xe gas-exchange MRI) has the potential to overcome these challenges. When participants inhale hyperpolarized 129Xe gas, it has different MR spectral properties as a gas, as it diffuses through the alveolar membrane and as it binds to red-blood-cells. 129Xe MR spectroscopy and imaging provides a way to tease out the different anatomic components of gas-exchange simultaneously and provides spatial information about where abnormalities may occur. In this thesis, I developed and applied 129Xe MR spectroscopy and imaging to measure gas-exchange in the lungs alongside other clinical and imaging measurements. I measured 129Xe gas-exchange in asymptomatic congenital heart disease and in prospective, controlled studies of long-COVID. I also developed mathematical tools to model 129Xe MR signals during acquisition and reconstruction. The insights gained from my work underscore the potential for 129Xe gas-exchange MRI biomarkers towards a better understanding of cardiopulmonary disease. My work also provides a way to generate a deeper imaging and physiologic understanding of gas-exchange in vivo in healthy participants and patients with chronic lung and heart disease

    Comparative Multiple Case Study into the Teaching of Problem-Solving Competence in Lebanese Middle Schools

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    This multiple case study investigates how problem-solving competence is integrated into teaching practices in private schools in Lebanon. Its purpose is to compare instructional approaches to problem-solving across three different programs: the American (Common Core State Standards and New Generation Science Standards), French (Socle Commun de Connaissances, de Compétences et de Culture), and Lebanese with a focus on middle school (grades 7, 8, and 9). The project was conducted in nine schools equally distributed among three categories based on the programs they offered: category 1 schools offered the Lebanese program, category 2 the French and Lebanese programs, and category 3 the American and Lebanese programs. Each school was treated as a separate case. Structured observation data were collected using observation logs that focused on lesson objectives and specific cognitive problem-solving processes. The two logs were created based on a document review of the requirements for the three programs. Structured observations were followed by semi-structured interviews that were conducted to explore teachers' beliefs and understandings of problem-solving competence. The comparative analysis of within-category structured observations revealed an instruction ranging from teacher-led practices, particularly in category 1 schools, to more student-centered approaches in categories 2 and 3. The cross-category analysis showed a reliance on cognitive processes primarily promoting exploration, understanding, and demonstrating understanding, with less emphasis on planning and executing, monitoring and reflecting, thus uncovering a weakness in addressing these processes. The findings of the post-observation semi-structured interviews disclosed a range of definitions of problem-solving competence prevalent amongst teachers with clear divergences across the three school categories. This research is unique in that it compares problem-solving teaching approaches across three different programs and explores underlying teachers' beliefs and understandings of problem-solving competence in the Lebanese context. It is hoped that this project will inform curriculum developers about future directions and much-anticipated reforms of the Lebanese program and practitioners about areas that need to be addressed to further improve the teaching of problem-solving competence

    Intelligent architecture to support second generation general accounting

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementThis study aimed to innovate the world of accounting software. After so many years, accountants are faced with an unbelievable amount of work, which is not always productive, effective and efficient for both the accountant and the company that provided him with the data required to carry out the accounting. There is already accounting software with various automation processes, from ornamentation to profitability analysis and management reporting. There is also software that is updated in accordance with the accounting laws, i.e., the platform changes its mechanisms according to the changes in the law. Despite the existence of this software, manual work remains, and the amount of information accountants are faced with is still very large. It is difficult for accountants to do a 100% reliable job with so much information and data they have. One of the most common situations in the accounting world is undoubtedly the miscalculation or forgetting of some financial or non-financial data found in accounting operations (income statements, balance sheets, etc.). To render accounting operations efficient, effective and productive, errorfree and 100% reliable, an intelligent architecture has been developed to support second generation general accounting. This architectural design was developed with a view to make the existing software smarter with the help of artificial intelligence. A study was carried out on accounting keys and concepts, on AI and main process automation techniques to build the model. With these studies it was intended to acquire all possible requirements for the creation of the architecture. Towards the end of the thesis the model was validated

    Multimodal spatio-temporal deep learning framework for 3D object detection in instrumented vehicles

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    This thesis presents the utilization of multiple modalities, such as image and lidar, to incorporate spatio-temporal information from sequence data into deep learning architectures for 3Dobject detection in instrumented vehicles. The race to autonomy in instrumented vehicles or self-driving cars has stimulated significant research in developing autonomous driver assistance systems (ADAS) technologies related explicitly to perception systems. Object detection plays a crucial role in perception systems by providing spatial information to its subsequent modules; hence, accurate detection is a significant task supporting autonomous driving. The advent of deep learning in computer vision applications and the availability of multiple sensing modalities such as 360° imaging, lidar, and radar have led to state-of-the-art 2D and 3Dobject detection architectures. Most current state-of-the-art 3D object detection frameworks consider single-frame reference. However, these methods do not utilize temporal information associated with the objects or scenes from the sequence data. Thus, the present research hypothesizes that multimodal temporal information can contribute to bridging the gap between 2D and 3D metric space by improving the accuracy of deep learning frameworks for 3D object estimations. The thesis presents understanding multimodal data representations and selecting hyper-parameters using public datasets such as KITTI and nuScenes with Frustum-ConvNet as a baseline architecture. Secondly, an attention mechanism was employed along with convolutional-LSTM to extract spatial-temporal information from sequence data to improve 3D estimations and to aid the architecture in focusing on salient lidar point cloud features. Finally, various fusion strategies are applied to fuse the modalities and temporal information into the architecture to assess its efficacy on performance and computational complexity. Overall, this thesis has established the importance and utility of multimodal systems for refined 3D object detection and proposed a complex pipeline incorporating spatial, temporal and attention mechanisms to improve specific, and general class accuracy demonstrated on key autonomous driving data sets

    Knowledge-based Modelling of Additive Manufacturing for Sustainability Performance Analysis and Decision Making

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    Additiivista valmistusta on pidetty käyttökelpoisena monimutkaisissa geometrioissa, topologisesti optimoiduissa kappaleissa ja kappaleissa joita on muuten vaikea valmistaa perinteisillä valmistusprosesseilla. Eduista huolimatta, yksi additiivisen valmistuksen vallitsevista haasteista on ollut heikko kyky tuottaa toimivia osia kilpailukykyisillä tuotantomäärillä perinteisen valmistuksen kanssa. Mallintaminen ja simulointi ovat tehokkaita työkaluja, jotka voivat auttaa lyhentämään suunnittelun, rakentamisen ja testauksen sykliä mahdollistamalla erilaisten tuotesuunnitelmien ja prosessiskenaarioiden nopean analyysin. Perinteisten ja edistyneiden valmistusteknologioiden mahdollisuudet ja rajoitukset määrittelevät kuitenkin rajat uusille tuotekehityksille. Siksi on tärkeää, että suunnittelijoilla on käytettävissään menetelmät ja työkalut, joiden avulla he voivat mallintaa ja simuloida tuotteen suorituskykyä ja siihen liittyvän valmistusprosessin suorituskykyä, toimivien korkea arvoisten tuotteiden toteuttamiseksi. Motivaation tämän väitöstutkimuksen tekemiselle on, meneillään oleva kehitystyö uudenlaisen korkean lämpötilan suprajohtavan (high temperature superconducting (HTS)) magneettikokoonpanon kehittämisessä, joka toimii kryogeenisissä lämpötiloissa. Sen monimutkaisuus edellyttää monitieteisen asiantuntemuksen lähentymistä suunnittelun ja prototyyppien valmistuksen aikana. Tutkimus hyödyntää tietopohjaista mallinnusta valmistusprosessin analysoinnin ja päätöksenteon apuna HTS-magneettien mekaanisten komponenttien suunnittelussa. Tämän lisäksi, tutkimus etsii mahdollisuuksia additiivisen valmistuksen toteutettavuuteen HTS-magneettikokoonpanon tuotannossa. Kehitetty lähestymistapa käyttää fysikaalisiin kokeisiin perustuvaa tuote-prosessi-integroitua mallinnusta tuottamaan kvantitatiivista ja laadullista tietoa, joka määrittelee prosessi-rakenne-ominaisuus-suorituskyky-vuorovaikutuksia tietyille materiaali-prosessi-yhdistelmille. Tuloksina saadut vuorovaikutukset integroidaan kaaviopohjaiseen malliin, joka voi auttaa suunnittelutilan tutkimisessa ja täten auttaa varhaisessa suunnittelu- ja valmistuspäätöksenteossa. Tätä varten testikomponentit valmistetaan käyttämällä kahta metallin additiivista valmistus prosessia: lankakaarihitsaus additiivista valmistusta (wire arc additive manufacturing) ja selektiivistä lasersulatusta (selective laser melting). Rakenteellisissa sovelluksissa yleisesti käytetyistä metalliseoksista (ruostumaton teräs, pehmeä teräs, luja niukkaseosteinen teräs, alumiini ja kupariseokset) testataan niiden mekaaniset, lämpö- ja sähköiset ominaisuudet. Lisäksi tehdään metalliseosten mikrorakenteen karakterisointi, jotta voidaan ymmärtää paremmin valmistusprosessin parametrien vaikutusta materiaalin ominaisuuksiin. Integroitu mallinnustapa yhdistää kerätyn kokeellisen tiedon, olemassa olevat analyyttiset ja empiiriset vuorovaikutus suhteet, sekä muut tietopohjaiset mallit (esim. elementtimallit, koneoppimismallit) päätöksenteon tukijärjestelmän muodossa, joka mahdollistaa optimaalisen materiaalin, valmistustekniikan, prosessiparametrien ja muitten ohjausmuuttujien valinnan, lopullisen 3d-tulosteun komponentin halutun rakenteen, ominaisuuksien ja suorituskyvyn saavuttamiseksi. Valmistuspäätöksenteko tapahtuu todennäköisyysmallin, eli Bayesin verkkomallin toteuttamisen kautta, joka on vankka, modulaarinen ja sovellettavissa muihin valmistusjärjestelmiin ja tuotesuunnitelmiin. Väitöstyössä esitetyn mallin kyky parantaa additiivisien valmistusprosessien suorituskykyä ja laatua, täten edistää kestävän tuotannon tavoitteita.Additive manufacturing (AM) has been considered viable for complex geometries, topology optimized parts, and parts that are otherwise difficult to produce using conventional manufacturing processes. Despite the advantages, one of the prevalent challenges in AM has been the poor capability of producing functional parts at production volumes that are competitive with traditional manufacturing. Modelling and simulation are powerful tools that can help shorten the design-build-test cycle by enabling rapid analysis of various product designs and process scenarios. Nevertheless, the capabilities and limitations of traditional and advanced manufacturing technologies do define the bounds for new product development. Thus, it is important that the designers have access to methods and tools that enable them to model and simulate product performance and associated manufacturing process performance to realize functional high value products. The motivation for this dissertation research stems from ongoing development of a novel high temperature superconducting (HTS) magnet assembly, which operates in cryogenic environment. Its complexity requires the convergence of multidisciplinary expertise during design and prototyping. The research applies knowledge-based modelling to aid manufacturing process analysis and decision making in the design of mechanical components of the HTS magnet. Further, it explores the feasibility of using AM in the production of the HTS magnet assembly. The developed approach uses product-process integrated modelling based on physical experiments to generate quantitative and qualitative information that define process-structure-property-performance interactions for given material-process combinations. The resulting interactions are then integrated into a graph-based model that can aid in design space exploration to assist early design and manufacturing decision-making. To do so, test components are fabricated using two metal AM processes: wire and arc additive manufacturing and selective laser melting. Metal alloys (stainless steel, mild steel, high-strength low-alloyed steel, aluminium, and copper alloys) commonly used in structural applications are tested for their mechanical-, thermal-, and electrical properties. In addition, microstructural characterization of the alloys is performed to further understand the impact of manufacturing process parameters on material properties. The integrated modelling approach combines the collected experimental data, existing analytical and empirical relationships, and other data-driven models (e.g., finite element models, machine learning models) in the form of a decision support system that enables optimal selection of material, manufacturing technology, process parameters, and other control variables for attaining desired structure, property, and performance characteristics of the final printed component. The manufacturing decision making is performed through implementation of a probabilistic model i.e., a Bayesian network model, which is robust, modular, and can be adapted for other manufacturing systems and product designs. The ability of the model to improve throughput and quality of additive manufacturing processes will boost sustainable manufacturing goals

    EXAMINING PROTEIN CONFORMATIONAL DYNAMICS USING COMPUTATIONAL TECHNIQUES: STUDIES ON PHOSPHATIDYLINOSITOL-3-KINASE AND THE SODIUM-IODIDE SYMPORTER

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    Experimental biophysics techniques used to study proteins, polymers of amino acids that comprise most therapeutic targets of human disease, face limitations in their ability to interrogate the continual structural fluctuations exhibited by these macromolecules in the context of their myriad cellular functions. This dissertation aims to illustrate case studies that demonstrate how protein conformational dynamics can be characterized using computational methods, yielding novel insights into their functional regulation and activity. Towards this end, the work presented here describes two specific membrane proteins of therapeutic relevance: Phosphoinositide 3-kinase (PI3Kα), and the Na+/I- symporter (NIS). The PI3KCA gene, encoding the catalytic subunit of the PI3Kα protein that phosphorylates phosphatidylinositol-4,5-bisphosphate (PIP2) to generate phosphatidylinositol-3,4,5-triphosphate (PIP3), is highly mutated in human cancer. As such, a deeper mechanistic understanding of PI3Kα could facilitate the development of novel chemotherapeutic approaches. The second chapter of this dissertation describes molecular dynamics (MD) simulations that were conducted to determine how PI3Kα conformations are influenced by physiological effectors and the nSH2 domain of a regulatory subunit, p85. The results reported here suggest that dynamic allostery plays a role in populating the catalytically competent conformation of PI3Kα. NIS, a thirteen-helix transmembrane protein found in the thyroid and other tissues, transports iodide, a required constituent of thyroid hormones T3 and T4. Despite extensive experimental information and clinical data, many mechanistic details about NIS remain unresolved. The third chapter of this dissertation describes the results of unbiased and enhanced-sampling MD simulations of inwardly and outwardly open models of bound NIS under an enforced ion gradient. Simulations of NIS in the absence or presence of perchlorate are also described. The work presented in this dissertation aims to add to our mechanistic understanding of NIS ion transport and elucidate conformational states that occur between the inward and outward transitions of NIS in the absence and presence of bound Na+ and I- ions, which can provide valuable insight into its physiological activity and inform therapeutic interventions. Taken together, these case studies demonstrate the ability of computational techniques to provide novel insights into the impact of structural dynamics on the functional regulation of therapeutically important biological macromolecules

    Examples of works to practice staccato technique in clarinet instrument

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    Klarnetin staccato tekniğini güçlendirme aşamaları eser çalışmalarıyla uygulanmıştır. Staccato geçişlerini hızlandıracak ritim ve nüans çalışmalarına yer verilmiştir. Çalışmanın en önemli amacı sadece staccato çalışması değil parmak-dilin eş zamanlı uyumunun hassasiyeti üzerinde de durulmasıdır. Staccato çalışmalarını daha verimli hale getirmek için eser çalışmasının içinde etüt çalışmasına da yer verilmiştir. Çalışmaların üzerinde titizlikle durulması staccato çalışmasının ilham verici etkisi ile müzikal kimliğe yeni bir boyut kazandırmıştır. Sekiz özgün eser çalışmasının her aşaması anlatılmıştır. Her aşamanın bir sonraki performans ve tekniği güçlendirmesi esas alınmıştır. Bu çalışmada staccato tekniğinin hangi alanlarda kullanıldığı, nasıl sonuçlar elde edildiği bilgisine yer verilmiştir. Notaların parmak ve dil uyumu ile nasıl şekilleneceği ve nasıl bir çalışma disiplini içinde gerçekleşeceği planlanmıştır. Kamış-nota-diyafram-parmak-dil-nüans ve disiplin kavramlarının staccato tekniğinde ayrılmaz bir bütün olduğu saptanmıştır. Araştırmada literatür taraması yapılarak staccato ile ilgili çalışmalar taranmıştır. Tarama sonucunda klarnet tekniğin de kullanılan staccato eser çalışmasının az olduğu tespit edilmiştir. Metot taramasında da etüt çalışmasının daha çok olduğu saptanmıştır. Böylelikle klarnetin staccato tekniğini hızlandırma ve güçlendirme çalışmaları sunulmuştur. Staccato etüt çalışmaları yapılırken, araya eser çalışmasının girmesi beyni rahatlattığı ve istekliliği daha arttırdığı gözlemlenmiştir. Staccato çalışmasını yaparken doğru bir kamış seçimi üzerinde de durulmuştur. Staccato tekniğini doğru çalışmak için doğru bir kamışın dil hızını arttırdığı saptanmıştır. Doğru bir kamış seçimi kamıştan rahat ses çıkmasına bağlıdır. Kamış, dil atma gücünü vermiyorsa daha doğru bir kamış seçiminin yapılması gerekliliği vurgulanmıştır. Staccato çalışmalarında baştan sona bir eseri yorumlamak zor olabilir. Bu açıdan çalışma, verilen müzikal nüanslara uymanın, dil atış performansını rahatlattığını ortaya koymuştur. Gelecek nesillere edinilen bilgi ve birikimlerin aktarılması ve geliştirici olması teşvik edilmiştir. Çıkacak eserlerin nasıl çözüleceği, staccato tekniğinin nasıl üstesinden gelinebileceği anlatılmıştır. Staccato tekniğinin daha kısa sürede çözüme kavuşturulması amaç edinilmiştir. Parmakların yerlerini öğrettiğimiz kadar belleğimize de çalışmaların kaydedilmesi önemlidir. Gösterilen azmin ve sabrın sonucu olarak ortaya çıkan yapıt başarıyı daha da yukarı seviyelere çıkaracaktır

    Improving sensor placement optimisation robustness to environmental variations and sensor failures for structural health monitoring systems

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    The installation of structural health monitoring (SHM) systems based on machine learning algorithms on structures has been of constant interest. The application of this kind of SHM system can facilitate decisions regarding maintenance and the remaining useful life of the structure in a more automatic and convenient way. As part of the SHM system to collect information, the sensor system can be optimally designed to improve the performance of the final system. In this thesis, the work focuses on how to consider the effects of environment and sensor failures during the sensor placement optimisation (SPO) to build a more robust and effective monitoring system. Since the availability of data during the design phase varies widely from project to project and there are no studies or specifications that provide specific guidance, not much research has been done on the design of such sensor systems, which require reliable simulated or measured data to be available during the design phase. Considering the different levels of data accessibility at the design phase, this thesis proposes a series of strategies for the optimal design of sensor systems for SHM systems from a machine-learning perspective. The first main content of this thesis is hierarchical assessment criteria of designed-system performance to balance the computational feasibility and visualisation of the final system performance. At the stage after data is collected, machine learning model results are often used as a criterion, whose acquisition is usually time-consuming. At the same time, higher data accuracy is required. Therefore, the criteria used in the design of sensor systems are divided into different tiers. The criteria for the initial stage can be abstracted from the purpose of the applied machine learning model to significantly reduce the number of candidate designs. The criteria for the final stage can be similar to those used in the stage after data is collected. Whether or not to use criteria from all tiers depends on the level of data availability. It can be found that more work on the optimisation design of the sensor system can be done at the initial stage of the hierarchical design framework. Therefore, the other three main contents of this thesis are developed at this stage. Considering different levels of data availability, supervised and unsupervised correlation-based strategies to evaluate sensor combinations are proposed, including the evaluation criterion and the fast calculation methods of this criterion. Sensor combinations can be ranked even if only the healthy state data are accessible. To account for the effects of environmental variations, two SPO strategies based on approaches to extracting robust features are proposed, and an appropriate criterion that can be used is also introduced. These two strategies cover both situations where environmental change information is available and not. To consider the sensor-failure effect in the SPO process, another two strategies, namely fail-safe sensor optimisation or fail-safe optimisation with redundancy, are proposed in this thesis, both of which can take into account the performance of the designed system before and after the failure of some critical sensors. Different assessment criteria are adopted to demonstrate the generality of these strategies
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